Dario Cositore
Dario Cositore - Agentic AI Systems Architect
I build personal AI agents, business agents and workflow systems that multiply human capability across CRM, intake, documents, APIs, files, dashboards and back-office execution. Existing stack or custom agent shell. Memory, tools, permissions, workflow state, execution loops and human approval built around real business constraints.
Mission
Enable every serious operator to have a persistent agentic layer that follows context, remembers work, calls tools, understands operating style and expands what one human can execute.
Proof Points
- 13+ frontier AI models evaluated
- 100+ real business workflows tested
- 189K+ market signals processed through Gapfeed
- 5 autonomous agents in production
- 45% qualified conversion lift in deployments
- Sub-10-second lead-to-CRM sync
- 30+ person teams led
- $400K monthly revenue scaled
Background
I combine hardcore business operations experience with frontier AI capabilities. I don't just recommend software. I architect native, autonomous pipelines that remove repetitive human tasks while preserving human judgment for exceptions and strategy.
What I Build
My work spans three categories: personal AI agents that operate as persistent, context-aware layers for solo operators and consultants; business agents that automate CRM intake, lead qualification, document processing and back-office execution; and custom agent shells that give companies full control over memory, permissions, tool access and approval flows without depending on third-party wrappers.
Every system I build is designed around one constraint: the human in the loop needs to be able to audit, override and expand the agent's scope without touching the underlying code. That means structured memory files, explicit tool schemas, deterministic approval checkpoints and logs that surface what the agent actually did.
Approach
I start with the workflow that is currently breaking or creating the most friction. Not the biggest vision. Not the full roadmap. The single constraint that, if removed, would immediately change the operator's capacity to execute. From there I map the data inputs, decision logic and output schema, then build the minimal agentic layer that handles it reliably before expanding scope.
Most AI deployments fail because the underlying process is poorly defined before automation is applied. I fix the process model first. Automation comes second. This is why the systems I build keep running after the engagement ends, rather than requiring constant maintenance or a dedicated AI operations team to manage.
Contact
Email: dario@dariocositore.com